Standard of Living of Tribal Households in Rural Areas of North-East India: A Principal Component Analysis Approach

2020 ◽  
pp. 598-605
Keyword(s):  
Principal Component Analysis ◽  
Rural Areas ◽  
Secondary Data ◽  
Principal Component ◽  
Component Analysis ◽  
Standard Of Living ◽  
Living Standard ◽  
North East ◽  
The North ◽  
East Region

The study is an attempt to construct a standard of living index (SLI) using principal component analysis method and to measure the living standard of tribal population in rural areas of the north-east region of India. The study stands on secondary data source namely census report 2011. The principal component analysis (PCA) method was deployed to analyse the data and deal with the objectives of the study. The study found that the North-East region as a whole belonged to the medium category in terms of living standard of tribal households. Mizoram ranked top among the north-eastern states by securing the highest living standard of Mizo tribes while Assam scored the lowest living standard of tribal communities.

scholarly journals Penggunaan Analisis Komponen Utama Dalam Penggabungan Data Peubah Ganda pada Kasus Produksi Pertanian dan Perkebunan Di Wilayah Bolaang Mongondow Tahun 2008

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (2) ◽  
pp. 1 ◽  
Author(s):  
Sunarsi Habib Abdurrachman ◽  
Hanny Komalig ◽  
Nelson Nainggolan
Keyword(s):  
Principal Component Analysis ◽  
Initial Data ◽  
Principal Components ◽  
Multivariate Data ◽  
Secondary Data ◽  
Principal Component ◽  
Component Analysis ◽  
The North ◽  
North Sulawesi ◽  
Production Agriculture

Abstract The objective of this research is to study the combine the two groups of data with multivariate variables using Principal Component Analysis. The data used in this study is a secondary data drawn from the North Sulawesi BPS data in Production Agriculture and Plantation Bolaang Mongondow region in 2008. The results show that PCA can be used to combining two separate groups multivariate data and the correlation between the Principal Components of the data are combined with the Principal Component of the overall initial data (intact) is relatively high wich correlation between PC1 and PC1AB as big 0,987 and correlation between PC2 and PC2AB as big 0,916. Keywords : Principal Component Analysis, Agriculture Production and Plantation Abstrak Tujuan penelitian ini adalah menggabungkan dua gugus data peubah ganda dengan menggunakan Analisis Komponen Utama. Data yang digunakan dalam penelitian ini merupakan data sekunder yang diambil dari BPS Sulawesi Utara yakni Data Produksi Pertanian Dan Perkebunan Di Wilayah Bolaang Mongondow Tahun 2008. Hasilnya menunjukkan bahwa AKU dapat digunakan untuk menggabungkan dua gugus data peubah ganda yang terpisah dan korelasi antara komponen utama dari data yang digabungkan dengan komponen utama dari keseluruhan data awal (utuh)  relatif tinggi yakni dengan nilai korelasi PC1 dan PC1AB sebesar 0,987 dan PC2 dan PC2AB  sebesar 0,916.   Kata kunci : Analisis Komponen Utama, Produksi Pertanian dan Perkebunan

A principal component analysis of the determinants of spatial disparity between rural and urban localities of Ghana

2017 ◽  
Vol 44 (6) ◽  
pp. 715-731 ◽  
Author(s):  
Ivy Drafor
Keyword(s):  
Principal Component Analysis ◽  
Rural Areas ◽  
Urban Areas ◽  
Principal Component ◽  
Component Analysis ◽  
Spatial Inequality ◽  
Necessary Condition ◽  
Spatial Disparity ◽  
Content Type ◽  
Rural And Urban

Purpose The purpose of this paper is to analyse the spatial disparity between rural and urban areas in Ghana using the Ghana Living Standards Survey’s (GLSS) rounds 5 and 6 data to advance the assertion that an endowed rural sector is necessary to promote agricultural development in Ghana. This analysis helps us to know the factors that contribute to the depravity of the rural sectors to inform policy towards development targeting. Design/methodology/approach A multivariate principal component analysis (PCA) and hierarchical cluster analysis were applied to data from the GLSS-5 and GLSS-6 to determine the characteristics of the rural-urban divide in Ghana. Findings The findings reveal that the rural poor also spend 60.3 per cent of their income on food, while the urban dwellers spend 49 per cent, which is an indication of food production capacity. They have low access to information technology facilities, have larger household sizes and lower levels of education. Rural areas depend a lot on firewood for cooking and use solar/dry cell energies and kerosene for lighting which have implications for conserving the environment. Practical implications Developing the rural areas to strengthen agricultural growth and productivity is a necessary condition for eliminating spatial disparities and promoting overall economic development in Ghana. Addressing rural deprivation is important for conserving the environment due to its increased use of fuelwood for cooking. Absence of alternatives to the use of fuelwood weakens the efforts to reduce deforestation. Originality/value The application of PCA to show the factors that contribute to spatial inequality in Ghana using the GLSS-5 and GLSS-6 data is unique. The study provides insights into redefining the framework for national poverty reduction efforts.

scholarly journals Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Variational Bayesian Principal Component Analysis

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2298 ◽  
Author(s):  
Wudong Li ◽  
Weiping Jiang ◽  
Zhao Li ◽  
Hua Chen ◽  
Qusen Chen ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Missing Data ◽  
Principal Component ◽  
Component Analysis ◽  
Common Mode ◽  
Variational Bayesian ◽  
North East ◽  
Position Time Series ◽  
Gnss Position Time Series

Removal of the common mode error (CME) is very important for the investigation of global navigation satellite systems’ (GNSS) error and the estimation of an accurate GNSS velocity field for geodynamic applications. The commonly used spatiotemporal filtering methods normally process the evenly spaced time series without missing data. In this article, we present the variational Bayesian principal component analysis (VBPCA) to estimate and extract CME from the incomplete GNSS position time series. The VBPCA method can naturally handle missing data in the Bayesian framework and utilizes the variational expectation-maximization iterative algorithm to search each principal subspace. Moreover, it could automatically select the optimal number of principal components for data reconstruction and avoid the overfitting problem. To evaluate the performance of the VBPCA algorithm for extracting CME, 44 continuous GNSS stations located in Southern California were selected. Compared to previous approaches, VBPCA could achieve better performance with lower CME relative errors when more missing data exists. Since the first principal component (PC) extracted by VBPCA is remarkably larger than the other components, and its corresponding spatial response presents nearly uniform distribution, we only use the first PC and its eigenvector to reconstruct the CME for each station. After filtering out CME, the interstation correlation coefficients are significantly reduced from 0.43, 0.46, and 0.38 to 0.11, 0.10, and 0.08, for the north, east, and up (NEU) components, respectively. The root mean square (RMS) values of the residual time series and the colored noise amplitudes for the NEU components are also greatly suppressed, with average reductions of 27.11%, 28.15%, and 23.28% for the former, and 49.90%, 54.56%, and 49.75% for the latter. Moreover, the velocity estimates are more reliable and precise after removing CME, with average uncertainty reductions of 51.95%, 57.31%, and 49.92% for the NEU components, respectively. All these results indicate that the VBPCA method is an alternative and efficient way to extract CME from regional GNSS position time series in the presence of missing data. Further work is still required to consider the effect of formal errors on the CME extraction during the VBPCA implementation.

Quality of Elementary Education in India’s Rural Areas: Results from Annual Status of Education Report (ASER) 2014

Social Change ◽  
2016 ◽  
Vol 46 (4) ◽  
pp. 544-559
Author(s):  
Sangram Charan Panigrahi
Keyword(s):  
Elementary Education ◽  
Rural Areas ◽  
Tamil Nadu ◽  
Critical Role ◽  
Principal Component ◽  
Elementary Level ◽  
North East ◽  
The North ◽  
Two Factors

The quality of elementary education plays a critical role in an individual’s growth. Thus it has the capacity to develop a nation’s human resources. This study has examined the capabilities of young students who enrolled at the elementary level in schools located in India’s rural areas and their proficiency in different subjects, specifically their own regional language, mathematics and their knowledge of English. In order to measure the performances of students enrolled in Classes 1–VIII, the study used the principal component analysis (PCA) on original, 12 correlated variables. A standardised regression score of two factors, generated from PCA, was used to measure the status of education at the elementary level for different states. By considering the total score of two factors, using the PCA, it was found that most of the states in the southern parts of the country, that is, Kerala, Andhra Pradesh, Telangana, Karnataka and Tamil Nadu, and the North-East region, that is, Assam, Mizoram, Tripura, Sikkim, Meghalaya, Nagaland and Arunachal Pradesh, exhibited better academic performances as compared to other states.

scholarly journals Multi-trait Analysis of Agroclimate Variations During the Growing Season in East-Central Poland (1971-2005)

2015 ◽  
Vol 29 (2) ◽  
pp. 213-219 ◽  
Author(s):  
Elżbieta Radzka ◽  
Katarzyna Rymuza
Keyword(s):  
Principal Component Analysis ◽  
Air Temperature ◽  
Meteorological Data ◽  
Principal Component ◽  
Growing Season ◽  
Component Analysis ◽  
The South ◽  
East Central ◽  
Central Poland ◽  
The North

Abstract The work is based on meteorological data recorded by nine stations of the Institute of Meteorology and Water Management located in east-central Poland from 1971 to 2005. The region encompasses the North Podlasian Lowland and the South Podlasian Lowland. Average values of selected agroclimate indicators for the growing season were determined. Moreover, principal component analysis was conducted to indicate elements that exerted the greatest influence on the agroclimate. Also, cluster analysis was carried out to select stations with similar agroclimate. Ward method was used for clustering and the Euclidean distance was applied. Principal component analysis revealed that the agroclimate of east-central Poland was predominantly affected by climatic water balance, number of days of active plant growth, length of the farming period, and the average air temperature during the growing season (Apr-Sept). Based on the analysis, the region of east-central Poland was divided into two groups (areas) with different agroclimatic conditions. The first area comprized the following stations: Szepietowo and Białowieża located in the North Podlasian Lowland and Biała Podlaska situated in the northern part of the South Podlasian Lowland. This area was characterized by shorter farming periods and a lower average air temperature during the growing season. The other group included the remaining stations located in the western part of both the Lowlands which was warmer and where greater water deficits were recorded.

scholarly journals Principal Component Analysis of the LEADER Approach (2007–2013) in South Western Europe (Extremadura and Alentejo)

2019 ◽  
Vol 11 (15) ◽  
pp. 4034 ◽  
Author(s):  
Nieto Masot ◽  
Alonso ◽  
Moreno
Keyword(s):  
Principal Component Analysis ◽  
Rural Development ◽  
Rural Areas ◽  
Western Europe ◽  
Principal Component ◽  
Component Analysis ◽  
Demographic Variables ◽  
Development Aid ◽  
Border Regions ◽  
Political Differences

Since the end of the last century, the Rural Development Policy and the associated Rural Development Aid have been implemented (according to the LEADER Approach) in European rural areas as a model of endogenous, integrated, and innovative development. Its objective is to reduce the differences of development in these areas. The objective of this paper is to analyze statistically (using Principal Component Analysis) the investments and projects carried out during the period of 2007–2013 in the regions of Extremadura and Alentejo. These two border regions have many territorial similarities but also historical, cultural, and political differences. These variations may contribute to a different implementation of the LEADER Approach. As determined by the results from the statistical analysis of economic aids and demographic variables, it is evident that there are differences in the management of the Rural Development Aid in both territories but resemblances in the results.

scholarly journals Different Crime Types in Western Cape Province: Principal Component Analysis

2016 ◽  
Vol 8 (5(J)) ◽  
pp. 18-26
Author(s):  
Kyei KA ◽  
Tshisikhawe TH ◽  
Dube LM
Keyword(s):  
Principal Component Analysis ◽  
Urban Areas ◽  
Secondary Data ◽  
Principal Component ◽  
Component Analysis ◽  
Western Cape ◽  
Western Cape Province ◽  
Factors Affecting ◽  
Crime Types ◽  
High Crime

South Africa has a very high crime rate compared to most countries. Crime affects the society, business and psychology of the people. It compels people to move out or come into a particular area. It is most prevalent in the urban areas where poverty gap is conspicuous. Western Cape and Gauteng Provinces are the best developed provinces in the country and therefore have higher crime levels. But the question is: what types of crime are prevalent in the Western Cape Province? And what are the major causes of these crimes? The purpose of this paper is to identify the different types of crimes committed in the Western Cape Province which are prominent. Principal Component analysis (PCA) has been use in this study to gauge the patterns of crime and the distinct important factors affecting the level of crime. Secondary data from a website have been used in the analysis. The results show that violence and vehicle thefts are the most committed crimes in the province. The areas where crime occurs most frequently are Bellville, Cape Town Central, Gugulethu, Harare, Khayelisha, Mitchells Plain, Nyanga and Parow. Firearms have been identified as major means for committing crime. The paper recommends that attempts be made by the provincial government to clamp down unlicensed fire arm holders/dealers. Amnesty should be granted to encourage holders of unlicensed fire arms to surrender without punishment and the public should report to the police all those dealing in unlicensed firearms in order to root out crime in the province.

scholarly journals A Characterisation and Profiling of District Health Indicators in Zimbabwe: An Application of Principal Component Analysis in a Data Limited Setting

10.36469/9833 ◽  
2015 ◽  
Vol 3 (2) ◽  
pp. 162-179
Author(s):  
Shepherd Shamu ◽  
Simbarashe Rusakaniko ◽  
Charles Hongoro
Keyword(s):  
Principal Component Analysis ◽  
Secondary Data ◽  
Principal Component ◽  
Health Indicators ◽  
Component Analysis ◽  
Health Seeking ◽  
District Health ◽  
Levels Of Care ◽  
Composite Indices ◽  
Equitable Allocation

Background: The Ministry of Health and Child Care, Zimbabwe does not have a method for prioritization and equitable allocation of its share of the national health budget and other resources in the sector. Regional allocations at the provincial level are made regardless of the provinces’ disease burden, population size, or needs. Currently there is no method available to show how the provinces eventually allocate these resources to the lower levels of care. In a data limited country such as Zimbabwe, Principal Component Analysis method can be used to identify a set of indicators that account for cross variation between different regions. This set of indicators could then be used by planners as reference indicators for equitable allocation of resources and prioritization of health care interventions. Objective: The aim of the study was to construct a set of simple, feasible, reliable and valid composite health indicators for use in characterising and profiling of the different districts in Zimbabwe. Method: This was a retrospective analysis of secondary data to derive composite indices for the 57 administrative health districts in Zimbabwe using routinely collected secondary data. The data was extracted from the 2012 Zimbabwe Health information database, the 2012 National Census and the 2011 Prices, Income and Expenditure Survey. Results: The analysis of the data resulted in the construction of 10 mutually exclusive principal composite indices, which included demographic, child related, disease related and health systems related indices. The 10 composite indices (population, immunisation, child mortality, antenatal care, HIV/TB, malaria, non-communicable diseases, socioeconomic, health seeking behaviour and infrastructure) were tested for construct and content validity and were found to be statistically robust, reliable and consistent with observed behaviour. Conclusion: The composite indices exhibited internal consistency and construct validity to be regarded as true representations of the cross variation of the 57 districts in Zimbabwe; hence these indices could be used to characterise the behaviour and assess the performance of these districts. There is also potential use for these indices in the areas of resource allocation and prioritisation of health interventions.

scholarly journals Economic perspective of indonesian rubber on agroforestry development

2021 ◽  
Vol 305 ◽  
pp. 02007
Author(s):  
Miftahul Azis ◽  
Saktyanu Kristyantoadi Dermoredjo ◽  
Bambang Sayaka ◽  
Linda Purwaningrat
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Rural Areas ◽  
Secondary Data ◽  
Principal Component ◽  
Relative Price ◽  
Economic Perspective ◽  
Global Demand ◽  
Group Members ◽  
Global Data

Rubber commodity is one of the plantation commodities that supports agroforestry, besides that it is one of the attractors in the creation of labor in rural areas. The economic linkages around intensive plantations will affect development of rubber in agroforestry areas. Rubber agroforestry is important because demands of global trade will encourage the development of agroforestry itself. For this reason, this paper can provide a description of the rubber economy in supporting the national economy. The data used is secondary data including national and global data, especially for countries of ITRC forum group members. The analytical method uses Principal Component Analysis which is expected to provide an overview of the conditions for the development of the national rubber economy. The results showed that 55.34 percent indicated that the economic development of rubber was already export-oriented, while 8.56 percent indicated that global production would affect the decline in rubber area, while 13.70 percent of the global relative price had its own characteristics. Thus, it can be seen that the challenges ahead, need to anticipate the development of rubber cultivation, both in monoculture and agroforestry plantations, which can support each other with an interconnected agribusiness system for harmony with global demand.

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